This disclosure relates generally to monitoring of brain wave signals. More particularly, the present invention relates to a mechanism for monitoring evolution of brain wave signals and to automatic detection of seizure activity in the brain wave signals.
About 5% of the world's population experiences seizure activity some times during their life. When seizures occur repeatedly without external stimulation, a person suffers from epilepsy. About 0.5% of the entire population belongs to that core group, which makes epilepsy the most common neurological disorder. According to the current standardization, there are two main categories of seizures: generalized and partial seizures. Generalized seizures involve the whole brain, while partial seizures involve a restricted area of the brain. The main categories are further divided to several subcategories, which describe the types of movements a person demonstrates and how the awareness and consciousness are affected during the seizure. In general, intense, paroxysmal, and involuntary muscle convulsions are called convulsions and are often related to seizures.
Electroencephalography (EEG) is a well-established method for assessing brain activity. Measurement electrodes are typically attached on the skin of the skull surface to record and analyze the weak biopotential signals generated in the pyramid cells of the cortex. Alternatively, electrodes may be attached invasively between the brain and skull, or inside the brain tissue. The EEG has been in wide use for decades in basic research of the neural systems of the brain as well as in the clinical diagnosis of various central nervous system diseases and disorders.
Documentation of behavior and EEG of epileptic patients offers important information for surgery planning, diagnosis and follow-up treatment of epilepsy. Because the seizures occur intermittently and unpredictably, long-term monitoring lasting for several days is typically used in order to catch enough information of the EEG and the behavioral manifestations related to seizures. These recordings are typically obtained in epilepsy monitoring units (EMUs) in hospitals where dedicated equipment and personnel are available for the purpose. Recent advances in the field of telemedicine and ambulatory recordings may, however, make home monitoring practicable for epileptic patients in the near future.
Long-term EEG recording produces a vast amount of EEG data, which is later reviewed by a certified specialist. In visual analysis, particular EEG waveform morphologies and dynamic patterns are searched for, which are known, based on experience, to correspond to seizures. Found morphologies and patterns are examined in detail for obtaining information about the type and origin of the seizure. As the visual analysis is based on pattern recognition conducted by a human observer, the analysis process contains certain limitations, such as subjectivity of seizure recognition and slowness of the analysis. Reviewing long-term EEG recordings may require several hours of work, and thus human brain may easily become exhausted and seizures may be missed, short ones in particular.
For aiding visual EEG review, automatic seizure detection algorithms have been developed since 1970's. However, because the EEG with seizure activity differs between patients, development of a universally functioning automatic detector is challenging. Recent advances in the field of automatic seizure detection are related to patient-specific seizure detectors, which are closing the performance gap between a human observer and computer based detectors. These detectors are semi-automatic; a human observer has to mark one seizure instance from the data before the detector can search for similar instances. Despite the recent advances in computing and the limitations of visual EEG review, it is still the state of the art of seizure detection.
Besides being important for diagnostic purposes, seizure detection has a vital role in care decisions aiming to prevent brain damage. If seizure activity does not relieve within a few minutes, the risk for irreversible brain damage increases drastically. Prolonged seizure activity is called status epilepticus (SE) and it is a major medical emergency. Patients suffering from SE are heavily treated in intensive care units (ICUs). Generalized SE leads to irreversible brain damage with lasting intellectual morbidity. Depending on the etiology, the mortality rate of generalized SE may be from 20 to 30%.
Within the last decade, the prevalence of seizures in ICU patients has been widely realized. It has been observed that even patients without a past history of epilepsy or any neurological disorder may express seizures in the ICU. The reason for these seizures may be related to critical illnesses, such as hypoxia, ischemia, intoxications, and metabolic abnormalities. Also, neurological pathologies like stroke, intracerebral hemorrhage, brain tumor, central nervous system infections, and traumatic brain injury increase the risk of seizures. What makes the seizure detection in this patient group especially challenging, is that a vast majority of the seizures are non-convulsive. That is, the patient does not exhibit intense movements during the seizure. According to the current knowledge, EEG is the only specific indicator of non-convulsive seizures. Actually, 18-34% of neurological intensive care patients suffering from unexplained depressed level of consciousness have been shown to have non-convulsive seizures and 10% of these patients are in non-convulsive status epilepticus (NCSE). According to the current understanding, non-convulsive seizures produce irreversible brain damage similarly as convulsive seizures do, and thus the medication is highly recommended for this patient group as well.
Seizure detection conducted for intensive care patients has set new requirements for automatic seizure detection algorithms. At the moment, these seizures are detected with the aid of continuous EEG monitoring and time-consuming visual EEG analysis. Seizures require acute treatment with anticonvulsants, and thus the delay related to visual reviewing is often detrimental to the patient. Consequently, there is an urgent need for automatic, on-line seizure detectors.
Commercially available automatic algorithms developed using data collected from the EMU's have not been evaluated properly for ICU patient population. In the EMUs, these detectors produce 0.6-2.4 false detections per hour. In the ICU environment, false positive rates are probable even higher, because the EEG of a neurologically ill ICU patient characteristically contains abnormal features closely resembling a seizure, such as triphasic waves and alpha coma. However, treating these abnormal EEG features with anticonvulsants may have detrimental effects to the patient. Therefore, reliable detection of seizure activity in the ICUs is especially important.
As described above, automatic seizure detection has remained a technical challenge for decades. New application areas, like ICU, and new knowledge of the criticality of non-convulsive seizures set new, more demanding criteria for the technical performance of automatic seizure detection. One signal feature that is observed by specialists in visual analysis is the time evolution of the seizure pattern. However, this criterion is practically omitted in known automated seizure detection algorithms. In their simplicity, known automatic EEG seizure detectors rely on signal characteristics like power and periodicity and are, thus, susceptible to false detections. Seizure evolution is characterized by sequential changes in the EEG, often visible in EEG frequency and amplitude. As commonly known, these changes are not specific for seizure activity only, because amplitude and frequency varies in neurologically healthy subjects as well, for example in relation to alterations in vigilance level.